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Exploring the Ability of Natural Language Processing to Extract Data From Nursing Narratives

HYUN, SOOKYUNG RN, DNSc; JOHNSON, STEPHEN B. PhD; BAKKEN, SUZANNE RN, DNSc

CIN: Computers, Informatics, Nursing:
doi: 10.1097/NCN.0b013e3181a91b58
Continuing Education
Abstract

Natural Language Processing (NLP) offers an approach for capturing data from narratives and creating structured reports for further computer processing. We explored the ability of a NLP system, Medical Language Extraction and Encoding (MedLEE), on nursing narratives. MedLEE extracted 490 concepts from narrative text in a sample of 553 oncology nursing process notes. The most frequently monitored and recorded signs and symptoms were related to chemotherapy care, such as adverse reactions, shortness of breath, nausea, pain, and bleeding. In terms of nursing interventions, chemotherapy, blood culture, medication, and blood transfusion were commonly recorded in free text. NLP may provide a feasible approach to extract data related to patient safety/quality measures and nursing outcomes by capturing nursing concepts that are not recorded through structured data entry. For better NLP performance in the domain of nursing, additional nursing terms and abbreviations must be added to MedLEE's lexicon.

Author Information

Author Affiliations: School of Nursing (Drs Hyun and Bakken); and Department of Biomedical Informatics (Drs Johnson and Bakken), Columbia University, New York.

This study was supported by 1R01LM07593 (S. Johnson, principal investigator) from the National Library of Medicine and P20NR007799 (S. Bakken, principal investigator) from the National Institute of Nursing Research.

Disclaimer: Authors declare no conflict of interest.

Corresponding author: Sookyung Hyun, RN, DNSc, 630 W 168th St, Mailbox 6, New York, NY 10032 (sh2138@columbia.edu).

© 2009 Lippincott Williams & Wilkins, Inc.